scholarly journals Accuracy and precision in super-resolution MRI: Enabling spherical tensor diffusion encoding at ultra-high b-values and high resolution

2021 ◽  
Author(s):  
Geraline Vis ◽  
Markus Nilsson ◽  
Carl-Fredrik Westin ◽  
Filip Szczepankiewicz
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Super Resolution ◽  
Signal Acquisition ◽  
Noise Floor ◽  
B Values ◽  
Low Snr ◽  
Accuracy And Precision ◽  
Signal Accuracy

Diffusion MRI (dMRI) is a useful probe of tissue microstructure but suffers from low signal-to-noise ratio (SNR) whenever high resolution and/or high diffusion encoding strengths are used. Low SNR leads not only to poor precision but also poor accuracy of the diffusion-weighted signal, as the rectified noise floor gives rise to a positive signal bias. Recently, super-resolution techniques have been proposed for signal acquisition at a low spatial resolution but high SNR, whereafter a higher spatial resolution is recovered by image reconstruction. In this work, we describe a super-resolution reconstruction framework for dMRI and investigate its performance with respect to signal accuracy and precision. Using strictly controlled phantom experiments, we show that the super-resolution approach improves accuracy by facilitating a more beneficial trade-off between spatial resolution and diffusion encoding strength before the noise floor affects the signal. Moreover, precision is shown to have a less straightforward dependency on acquisition, reconstruction, and intrinsic tissue parameters. Indeed, we find that a gain in precision from super-resolution reconstruction (SRR) is substantial only when some spatial resolution is sacrificed. We also demonstrated the value of SRR in the challenging combination of high resolution and spherical b-tensor encoding at ultrahigh b-values -- a configuration that produces a unique contrast that emphasizes tissue in which diffusion is restricted in all directions. We conclude that SRR is most valuable in low-SNR conditions, where it can suppress rectified noise floor effects and recover signal with high accuracy. The in vivo application showcases a vastly superior image contrast when using SRR compared to conventional imaging, facilitating investigations of brain tissue that would otherwise have prohibitively low SNR, resolution or required non-conventional MRI hardware.

scholarly journals Deriving VIIRS High-Spatial Resolution Water Property Data over Coastal and Inland Waters Using Deep Convolutional Neural Network

2021 ◽  
Vol 13 (10) ◽  
pp. 1944
Author(s):  
Xiaoming Liu ◽  
Menghua Wang
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Ocean Color ◽  
Super Resolution ◽  
Inland Waters ◽  
Coastal Oceans ◽  
Chl A ◽  
Color Data

The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite has been a reliable source of ocean color data products, including five moderate (M) bands and one imagery (I) band normalized water-leaving radiance spectra nLw(λ). The spatial resolutions of the M-band and I-band nLw(λ) are 750 m and 375 m, respectively. With the technique of convolutional neural network (CNN), the M-band nLw(λ) imagery can be super-resolved from 750 m to 375 m spatial resolution by leveraging the high spatial resolution features of I1-band nLw(λ) data. However, it is also important to enhance the spatial resolution of VIIRS-derived chlorophyll-a (Chl-a) concentration and the water diffuse attenuation coefficient at the wavelength of 490 nm (Kd(490)), as well as other biological and biogeochemical products. In this study, we describe our effort to derive high-resolution Kd(490) and Chl-a data based on super-resolved nLw(λ) images at the VIIRS five M-bands. To improve the network performance over extremely turbid coastal oceans and inland waters, the networks are retrained with a training dataset including ocean color data from the Bohai Sea, Baltic Sea, and La Plata River Estuary, covering water types from clear open oceans to moderately turbid and highly turbid waters. The evaluation results show that the super-resolved Kd(490) image is much sharper than the original one, and has more detailed fine spatial structures. A similar enhancement of finer structures is also found in the super-resolved Chl-a images. Chl-a filaments are much sharper and thinner in the super-resolved image, and some of the very fine spatial features that are not shown in the original images appear in the super-resolved Chl-a imageries. The networks are also applied to four other coastal and inland water regions. The results show that super-resolution occurs mainly on pixels of Chl-a and Kd(490) features, especially on the feature edges and locations with a large spatial gradient. The biases between the original M-band images and super-resolved high-resolution images are small for both Chl-a and Kd(490) in moderately to extremely turbid coastal oceans and inland waters, indicating that the super-resolution process does not change the mean values of the original images.

scholarly journals Super Resolution Digital Image Correlation (SR-DIC): an Alternative to Image Stitching at High Magnifications

2021 ◽  
Author(s):  
R. S. Hansen ◽  
D. W. Waldram ◽  
T. Q. Thai ◽  
R. B. Berke
Keyword(s):  
High Resolution ◽  
Digital Image Correlation ◽  
Spatial Resolution ◽  
Digital Image ◽  
Super Resolution ◽  
Image Correlation ◽  
Low Resolution ◽  
Strain Measurements ◽  
Resolution Image ◽  
High Resolution Image

Abstract Background High-resolution Digital Image Correlation (DIC) measurements have previously been produced by stitching of neighboring images, which often requires short working distances. Separately, the image processing community has developed super resolution (SR) imaging techniques, which improve resolution by combining multiple overlapping images. Objective This work investigates the novel pairing of super resolution with digital image correlation, as an alternative method to produce high-resolution full-field strain measurements. Methods First, an image reconstruction test is performed, comparing the ability of three previously published SR algorithms to replicate a high-resolution image. Second, an applied translation is compared against DIC measurement using both low- and super-resolution images. Third, a ring sample is mechanically deformed and DIC strain measurements from low- and super-resolution images are compared. Results SR measurements show improvements compared to low-resolution images, although they do not perfectly replicate the high-resolution image. SR-DIC demonstrates reduced error and improved confidence in measuring rigid body translation when compared to low resolution alternatives, and it also shows improvement in spatial resolution for strain measurements of ring deformation. Conclusions Super resolution imaging can be effectively paired with Digital Image Correlation, offering improved spatial resolution, reduced error, and increased measurement confidence.

scholarly journals Quantifying the effects of spatial resolution and noise on galaxy metallicity gradients

2020 ◽  
Vol 495 (4) ◽  
pp. 3819-3838
Author(s):  
Ayan Acharyya ◽  
Mark R Krumholz ◽  
Christoph Federrath ◽  
Lisa J Kewley ◽  
Nathan J Goldbaum ◽  
...  
Keyword(s):  
High Resolution ◽  
Galaxy Evolution ◽  
Spatial Resolution ◽  
Signal To Noise Ratio ◽  
Low Snr ◽  
Integral Field Spectroscopy ◽  
History Of ◽  
Gradient Measurements ◽  
Integral Field ◽  
Scale Length

ABSTRACT Metallicity gradients are important diagnostics of galaxy evolution, because they record the history of events such as mergers, gas inflow, and star formation. However, the accuracy with which gradients can be measured is limited by spatial resolution and noise, and hence, measurements need to be corrected for such effects. We use high-resolution (∼20 pc) simulation of a face-on Milky Way mass galaxy, coupled with photoionization models, to produce a suite of synthetic high-resolution integral field spectroscopy (IFS) datacubes. We then degrade the datacubes, with a range of realistic models for spatial resolution (2−16 beams per galaxy scale length) and noise, to investigate and quantify how well the input metallicity gradient can be recovered as a function of resolution and signal-to-noise ratio (SNR) with the intention to compare with modern IFS surveys like MaNGA and SAMI. Given appropriate propagation of uncertainties and pruning of low SNR pixels, we show that a resolution of 3–4 telescope beams per galaxy scale length is sufficient to recover the gradient to ∼10–20 per cent uncertainty. The uncertainty escalates to ∼60 per cent for lower resolution. Inclusion of the low SNR pixels causes the uncertainty in the inferred gradient to deteriorate. Our results can potentially inform future IFS surveys regarding the resolution and SNR required to achieve a desired accuracy in metallicity gradient measurements.

AN APPLICATION OF MATTING LAPLACIAN MATRIX TO RANGE IMAGE SUPER-RESOLUTION

2011 ◽  
Vol 08 (04) ◽  
pp. 273-280
Author(s):  
YUXIANG YANG ◽  
ZENGFU WANG
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Super Resolution ◽  
Laplacian Matrix ◽  
Range Data ◽  
Range Image ◽  
Range Images ◽  
High Quality ◽  
Camera Image ◽  
Image Super Resolution

This paper describes a successful application of Matting Laplacian Matrix to the problem of generating high-resolution range images. The Matting Laplacian Matrix in this paper exploits the fact that discontinuities in range and coloring tend to co-align, which enables us to generate high-resolution range image by integrating regular camera image into the range data. Using one registered and potentially high-resolution camera image as reference, we iteratively refine the input low-resolution range image, in terms of both spatial resolution and depth precision. We show that by using such a Matting Laplacian Matrix, we can get high-quality high-resolution range images.

scholarly journals Self-Dictionary Regression for Hyperspectral Image Super-Resolution

2018 ◽  
Vol 10 (10) ◽  
pp. 1574 ◽  
Author(s):  
Dongsheng Gao ◽  
Zhentao Hu ◽  
Renzhen Ye
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Spectral Resolution ◽  
Cross Correlation ◽  
Hyperspectral Image ◽  
High Spatial Resolution ◽  
Super Resolution ◽  
High Spectral Resolution ◽  
Low Resolution ◽  
Image Super Resolution

Due to sensor limitations, hyperspectral images (HSIs) are acquired by hyperspectral sensors with high-spectral-resolution but low-spatial-resolution. It is difficult for sensors to acquire images with high-spatial-resolution and high-spectral-resolution simultaneously. Hyperspectral image super-resolution tries to enhance the spatial resolution of HSI by software techniques. In recent years, various methods have been proposed to fuse HSI and multispectral image (MSI) from an unmixing or a spectral dictionary perspective. However, these methods extract the spectral information from each image individually, and therefore ignore the cross-correlation between the observed HSI and MSI. It is difficult to achieve high-spatial-resolution while preserving the spatial-spectral consistency between low-resolution HSI and high-resolution HSI. In this paper, a self-dictionary regression based method is proposed to utilize cross-correlation between the observed HSI and MSI. Both the observed low-resolution HSI and MSI are simultaneously considered to estimate the endmember dictionary and the abundance code. To preserve the spectral consistency, the endmember dictionary is extracted by performing a common sparse basis selection on the concatenation of observed HSI and MSI. Then, a consistent constraint is exploited to ensure the spatial consistency between the abundance code of low-resolution HSI and the abundance code of high-resolution HSI. Extensive experiments on three datasets demonstrate that the proposed method outperforms the state-of-the-art methods.

High spatiotemporal resolution and low photo-toxicity fluorescence imaging in live cells and in vivo

2019 ◽  
Vol 47 (6) ◽  
pp. 1635-1650 ◽  
Author(s):  
Xiaohong Peng ◽  
Xiaoshuai Huang ◽  
Ke Du ◽  
Huisheng Liu ◽  
Liangyi Chen
Keyword(s):  
Fluorescence Microscopy ◽  
Spatial Resolution ◽  
Cell Biology ◽  
Critical Role ◽  
Super Resolution ◽  
Light Sheet ◽  
Live Cells ◽  
Spatiotemporal Resolution ◽  
And Function

Taking advantage of high contrast and molecular specificity, fluorescence microscopy has played a critical role in the visualization of subcellular structures and function, enabling unprecedented exploration from cell biology to neuroscience in living animals. To record and quantitatively analyse complex and dynamic biological processes in real time, fluorescence microscopes must be capable of rapid, targeted access deep within samples at high spatial resolutions, using techniques including super-resolution fluorescence microscopy, light sheet fluorescence microscopy, and multiple photon microscopy. In recent years, tremendous breakthroughs have improved the performance of these fluorescence microscopies in spatial resolution, imaging speed, and penetration. Here, we will review recent advancements of these microscopies in terms of the trade-off among spatial resolution, sampling speed and penetration depth and provide a view of their possible applications.

scholarly journals Comparison of Super Resolution Reconstruction Acquisition Geometries for Use in Mouse Phenotyping

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Niranchana Manivannan ◽  
Bradley D. Clymer ◽  
Anna Bratasz ◽  
Kimerly A. Powell
Keyword(s):  
Spatial Resolution ◽  
3D Imaging ◽  
High Spatial Resolution ◽  
Super Resolution ◽  
Acquisition Time ◽  
Slice Thickness ◽  
Low Resolution ◽  
Voxel Size ◽  
Orthogonal Geometry

3D isotropic imaging at high spatial resolution (30–100 microns) is important for comparing mouse phenotypes. 3D imaging at high spatial resolutions is limited by long acquisition times and is not possible in manyin vivosettings. Super resolution reconstruction (SRR) is a postprocessing technique that has been proposed to improve spatial resolution in the slice-select direction using multiple 2D multislice acquisitions. Any 2D multislice acquisition can be used for SRR. In this study, the effects of using three different low-resolution acquisition geometries (orthogonal, rotational, and shifted) on SRR images were evaluated and compared to a known standard. Iterative back projection was used for the reconstruction of all three acquisition geometries. The results of the study indicate that super resolution reconstructed images based on orthogonally acquired low-resolution images resulted in reconstructed images with higher SNR and CNR in less acquisition time than those based on rotational and shifted acquisition geometries. However, interpolation artifacts were observed in SRR images based on orthogonal acquisition geometry, particularly when the slice thickness was greater than six times the inplane voxel size. Reconstructions based on rotational geometry appeared smoother than those based on orthogonal geometry, but they required two times longer to acquire than the orthogonal LR images.

scholarly journals Spatial Super Resolution of Real-World Aerial Images for Image-Based Plant Phenotyping

2021 ◽  
Vol 13 (12) ◽  
pp. 2308
Author(s):  
Masoomeh Aslahishahri ◽  
Kevin G. Stanley ◽  
Hema Duddu ◽  
Steve Shirtliffe ◽  
Sally Vail ◽  
...  
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Real World ◽  
Super Resolution ◽  
Aerial Images ◽  
Plant Phenotyping ◽  
Low Resolution ◽  
High Resolution Images ◽  
Synthetic Images ◽  
Low Resolution Images

Unmanned aerial vehicle (UAV) imaging is a promising data acquisition technique for image-based plant phenotyping. However, UAV images have a lower spatial resolution than similarly equipped in field ground-based vehicle systems, such as carts, because of their distance from the crop canopy, which can be particularly problematic for measuring small-sized plant features. In this study, the performance of three deep learning-based super resolution models, employed as a pre-processing tool to enhance the spatial resolution of low resolution images of three different kinds of crops were evaluated. To train a super resolution model, aerial images employing two separate sensors co-mounted on a UAV flown over lentil, wheat and canola breeding trials were collected. A software workflow to pre-process and align real-world low resolution and high-resolution images and use them as inputs and targets for training super resolution models was created. To demonstrate the effectiveness of real-world images, three different experiments employing synthetic images, manually downsampled high resolution images, or real-world low resolution images as input to the models were conducted. The performance of the super resolution models demonstrates that the models trained with synthetic images cannot generalize to real-world images and fail to reproduce comparable images with the targets. However, the same models trained with real-world datasets can reconstruct higher-fidelity outputs, which are better suited for measuring plant phenotypes.

scholarly journals Evaluating High Spatial Resolution Diffusion Kurtosis Imaging at 3T: Reproducibility and Quality of Fit

2020 ◽  
Author(s):  
Loxlan W. Kasa ◽  
Roy A.M. Haast ◽  
Tristan K. Kuehn ◽  
Farah N. Mushtaha ◽  
Corey A. Baron ◽  
...  
Keyword(s):  
White Matter ◽  
High Resolution ◽  
Diffusion Kurtosis Imaging ◽  
List Type ◽  
Whole Brain ◽  
B Values ◽  
Phantom Data ◽  
Diffusion Kurtosis ◽  
Fitting Accuracy

ABSTRACTBackgroundDiffusion kurtosis imaging (DKI) quantifies the microstructure’s non-Gaussian diffusion properties. However, it has increased fitting parameters and requires higher b-values. Evaluation of DKI reproducibility is important for clinical purposes.PurposeTo assess reproducibility in whole-brain high resolution DKI at varying b-values.Study TypeProspective.Subjects and PhantomsForty-four individuals from the test-retest Human Connectome Project (HCP) database and twelve 3D-printed tissue mimicking phantoms.Field Strength/SequenceMultiband echo-planar imaging for in vivo and phantom diffusion-weighted imaging at 3T and 9.4T respectively. MPRAGE at 3T for in vivo structural data.AssessmentFrom HCP data with b-value =1000,2000,3000 s/mm2 (dataset A), two additional datasets with b-values=1000, 3000 s/mm2 (dataset B) and b-values=1000, 2000 s/mm2 (dataset C) were extracted. Estimated DKI metrics from each dataset were used for evaluating reproducibility and fitting quality in whole-brain white matter (WM), region of interest (ROI) and gray matter (GM).Statistical TestsDKI reproducibility was assessed using the within-subject coefficient of variation (CoV), fitting residuals to evaluate DKI fitting accuracy and Pearson’s correlation to investigate presence of systematic biases.ResultsCompared to dataset C, the CoV from DKI parameters from datasets A and B were comparable, with WM and GM CoVs <20%, while differences between datasets were smaller for the DKI-derived DTI parameters. Slightly higher fitting residuals were observed in dataset C compared to A and B, but lower residuals in dataset B were detected for the WM ROIs. A similar trend was observed for the phantom data with comparable CoVs at varying fiber orientations for datasets A and B. In addition, dataset C was characterized by higher residuals across the different fiber crossings.Data ConclusionThe comparable reproducibility of DKI maps between datasets A and B observed in the in vivo and phantom data indicates that high reproducibility can still be achieved within a reasonable scan time, supporting DKI for clinical purposes.HIGHLIGHTS:Reproducibility and fitting accuracy of high resolution DKI were evaluated as function of available b-values.A DKI dataset with b-values of 1000 and 3000 s/mm2 performs equally well as the original HCP three-shell dataset, while a dataset with b-values of 1000 and 2000 s/mm2 has lower reproducibility and fitting quality.In vivo results were verified using phantoms capable of mimicking different white matter configurations.These results suggest that DKI data can be obtained within less time, without sacrificing data quality.

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